Thermography image processing with neural networks to identify corrosion under insulation (CUI)

ABSTRACT

A method for identifying corrosion under insulation (CUI) in a structure comprises receiving thermographs from the structure using an infrared camera, applying filters to the thermograph using a first machine learning system, initially determining a CUI classification based on output from the filters, and validating the initial CUI classification by an inspection of the structure. The first machine learning system is trained using results of the validation. Outputs of the first machine learning system and additional structural and environmental data are fed into a second machine learning system that incorporates information from earlier states into current states. The second machine learning system is trained to identify CUI according to changes in the outputs of the first machine learning system and the additional data over time until a second threshold for CUI classification accuracy is reached. CUI is thereafter identified using the first and second machine learning systems in coordination.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This is a Continuation of U.S. application Ser. No. 16/711,099, filedDec. 11, 2019, which is a Continuation of U.S. application Ser. No.15/712,490, filed Sep. 22, 2017, both of which are hereby incorporatedby reference in their respective entireties.

FIELD OF THE INVENTION

The present invention relates to inspection technologies, and inparticular relates to a method and system in which thermography imageprocessing is used with neural networks to identify corrosion underinsulation (CUI).

BACKGROUND OF THE INVENTION

Corrosion under insulation (CUI) is a condition in which an insulatedstructure such as a metal pipe suffers corrosion on the metal surfacebeneath the insulation. As the corrosion cannot be easily observed dueto the insulation covering, which typically surrounds the entirestructure, CUI is challenging to detect. The typical causes of CUI aremoisture buildup that infiltrates into the insulation material. Watercan accumulate in the annular space between the insulation and the metalsurface, causing surface corrosion. Sources of water that can inducecorrosion include rain, water leaks, and condensation, cooling watertower drift, deluge systems and steam tracing leaks. While corrosionusually begins locally, it can progress at high rates if there arerepetitive thermal cycles or contaminants in the water medium such aschloride or acid.

Studies indicate that 40 to 60% of pipe maintenance costs are caused byCUI. When CUI is undetected, the results of can lead to the shutdown ofa process unit or an entire facility, and can lead to catastrophicincidents. Since it is a hidden corrosion mechanism, the damage remainsunnoticed until insulation is removed or advanced NDT (non-destructivetesting) techniques, such as infrared thermography, are used toascertain the metal condition beneath the insulation. Removal ofinsulation can be a time-consuming and costly process, while theaccuracy NDT techniques can be insufficient due to the large number ofvariables (e.g., geometrical, environmental, material-related), thatcause false positives (incorrect detection of corrosion) and falsenegatives (incorrect non-detection of corrosion) in the detectionprocess.

What is therefore needed is improvement in the NDT detection processthat will enable external and confounding variables to be more properlyaccounted for so as to improve the accuracy of CUI detection.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a computer-implementedmethod for identifying corrosion under insulation (CUI) in a structure.The method comprises: a) receiving a thermograph captured from thestructure using an infrared radiation sensor; b) applying one or morefilters to the thermograph using a first machine learning system; c)initially determining a CUI classification based on output from the oneor more filters; d) validating the initial CUI classification by aninspection of the structure; e) training the filters of the firstmachine learning system based on results of the validation; f) repeatingsteps a) through e) with additional thermograph data until a firstthreshold for CUI classification accuracy is reached. In further stepsoutputs of the first machine learning system and additional data relatedto the structure and environment conditions are input into a secondmachine learning system that incorporates information from earlierstates into current states, and the second machine learning system istrained to identify CUI according to changes in the outputs of the firstmachine learning system and the additional data over time until a secondthreshold for CUI classification accuracy is reached. After the firstand second thresholds are reached, CUI is identified in the structurebased on current thermograph and additional data using the first andsecond machine learning systems in coordination.

In some embodiments, the first machine learning system includes aconvolutional neural network. Implementations of the convolutionalneural network can include a plurality of hierarchical layers, eachhierarchical layer including a convolutional stage, a non-linearfunction stage and a pooling stage.

In some embodiments, the second machine learning system includes arecurrent neural network.

In some implementations, the additional data input to the second machinelearning system includes ambient temperature, physical characteristicsof the structure and weather conditions measured over time.Advantageously, the first and second machine learning systems can betrained to recognize false positive findings relative to reflection ofinfrared radiation from objects external from the structure.

Identification of CUI can include identifying vulnerable areas of thestructure under the insulation that confine moisture with a highlikelihood. In some implementations, validation is performed using atleast two of the following techniques: pulsed eddy current evaluation,visual inspection, insulation removal and ultrasonic testing of wallthinning.

In further implementations, comprising preprocessing the thermographdata and the additional data to encode categorical variables andnormalize continuous variables. Thermograph data, environmentalvariables, and structural information can be vectorized and used asinputs to the neural networks.

Embodiments of the present invention also provide a system foridentifying corrosion under insulation (CUI) in a structure thatcomprises an infrared camera including a communication module that ispositioned so as to capture infrared radiation emitted from thestructure and adapted to communicate thermographs of the capturedradiation via the communication module, and a computer system includinga processor, memory and a communication module. The processor of thecomputer system is configured to execute a program that performs stepsof: i) applying one or more filters to thermographs received from theinfrared camera via the communication module, using a first machinelearning system; ii) initially determining a CUI classification based onoutput from the one or more filters; iii) validating the initial CUIclassification by an inspection of the structure; and iv) training thefilters of the first machine learning system based on results of thevalidation. The processor is configured to repeat steps i) through iv)with additional thermograph data until a first threshold for CUIclassification accuracy is reached. Outputs of the first machinelearning system and additional data related to the structure andenvironment conditions are input into a second machine learning systemthat incorporates information from earlier states into current states.The second machine learning system is trained to identify CUI accordingto changes in the outputs of the first machine learning system and theadditional data over time until a second threshold for CUIclassification accuracy is reached. After the first and secondthresholds are reached, CUI is identified in the structure based oncurrent thermograph and additional data using the first and secondmachine learning systems in coordination.

Any combinations of the various embodiments and implementationsdisclosed herein can be used.

These and other aspects, features, and advantages can be appreciatedfrom the following description of certain embodiments of the inventionand the accompanying drawing figures and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system for detecting CUI usingthermal imaging according to an embodiment of the present invention.

FIG. 2 is a schematic illustration of a method of thermal imageprocessing with neural network for identification of CUI according to anembodiment of the present invention.

FIG. 3 is a schematic illustration of an exemplary convolutional neuralnetwork that can be used in the context of the present invention.

FIG. 4 is a schematic illustration showing an example of how convolutioncan be applied to image data.

FIG. 5 is a schematic illustration of an exemplary recurrent neuralnetwork (RNN) 500 that can be used in the context of the presentinvention.

It is noted that the drawings are illustrative and not necessarily toscale.

DETAILED DESCRIPTION CERTAIN OF EMBODIMENTS OF THE INVENTION

The embodiments disclosed herein relates to a system and method for thepredictive detection of corrosion under insulation (CUI) considering alldependent and independent surrounding variables. The method includescapturing thermal image data (thermographs) of a structure usingmultiple IR (infrared) cameras over time, processing and analyzing thethermograph data to form an initial determination as to whether one ormore anomalies are present in the structure that are indicative ofcorrosion, and performing subsequent inspection (validation) to assessthe accuracy of the initial anomaly analysis. The results of thevalidation process are fed back in a supervised training process toimprove the accuracy of anomaly analysis and classification over time.Additional evaluations can be done using electromagnetic NDT for theanomalies found by the thermography techniques to find correlativerelationships and to increase the probability of CUI detection.

A principal purpose of the analysis and training is to enable thelearning system to properly distinguish anomalies given the currentenvironmental, structural and experimental parameters. A large number ofparameters are accounted for in this process to prevent false positiveand false negative determinations. As an example, detection of a hotspot on a structure while the structure is directly exposed to sunlightis not an anomalous condition. In contrast, if the hot spot occurs inthe absence of sunlight and/or other external conditions that could beexpected to cause the hot spot, an initial conclusion can be drawn thatthe insulation has been compromised in some way. In the latter case, theanomaly is recorded, and then after inspection, a report is generated totrain the algorithm as to verify whether the recorded anomaly was anaccurate identification of CU. When done repeatedly, the verificationand feedback causes the software to be more “intelligent” over time.

A number of different machine learning algorithms in differentcombinations can be used without limitation. One embodiment of a machinelearning process that has proven particularly useful includes acombination of “convolutional” (CNN) and “recurrent” (RNN) neuralnetworks. CNNs are particularly useful for stratifying thermal imagesinto abstraction levels according to image topology, while RNNs areparticularly useful for detecting patterns over time. Both factors areimportant, as the focus is on both detecting hotspots and on determiningtheir development over time. In addition, in some embodiments, boostingalgorithms, such as “Adaboost,” can be used in conjunction with CNNs andRNNs in order to achieve higher accuracies at the expense of morecomputational time. Since mistakes are extremely costly, increasingaccuracy at the sacrifice of computational time is an acceptabletrade-off.

FIG. 1 is a schematic illustration of a system 100 for detecting CUIusing thermal imaging according to an embodiment of the presentinvention. An exemplary structure 102 to be tested for CUI includes apipe 105 that is surrounded by a layer of insulation 110. The insulationcan comprise a foam layer with a thin metal wrap, or any otherconventional structural insulation. There is an interface 115 positionedat the junction between the external surface of the pipe 105 and theinsulation 110 in which water can accumulate and induce corrosion anddegradation of the underlying pipe. In the illustration of FIG. 1, twoareas where corrosion has built up on the pipe surface 122, 124 areshown.

In order to detect the corrosion beneath the insulation, an infraredcamera 130 mounted on a movable platform 135 such as a tripod ispositioned to take capture infrared radiation emitted from the structure102 at various longitudinal positions and angles so as to capture datafrom the entire length and circumference of the structure. In alternateembodiments suited for structures in locations that are difficult toaccess, such as high-up pipe installations the infrared camera 130 canbe mounted on an unmanned aerial vehicle (e.g., a drone). The unmannedaerial vehicle can be controlled remotely by inspection personnel totravel to structures of interest in such locations for gathering thermalimage data. The platform 135 or UAV can include additional sensors fordetecting environmental conditions including humidity, air pressure andwind speed. Thermal images, or thermographs, of the infrared radiationcaptured by the infrared camera 130 reveal temperature differentials andcontrasts underneath the insulation that are undetectable in the visiblespectrum radiation. More specifically, the IR thermal pattern on thesurface of the pipeline is caused by heat conducted from the warm innerpipe through the insulation to an outer wrap.

The infrared camera 130 preferably captures thermographs representativeof infrared radiation received from regions of the structure 103continuously over a selected (long) duration. The camera 130 is adaptedto convert the thermographs into a computer-readable file format (i.e.,thermograph files 137 shown in FIG. 1). The thermograph files 135 arepreferably communicated over a wireless communication mode (e.g.,Bluetooth, Wi-Fi) to an onsite computer system 140 for furtherprocessing. In other implementations, the thermograph files 135 can becommunicated over a wired connection, or alternatively, can be stored ina memory medium (e.g., SD card, flash drive) at the infrared camera andthen manually transferred to the computer system 140.

Computer system 140 can be a tablet, laptop, smart phone or any otherportable computing device capable of executing programs used fortraining a machine learning algorithm to detect CUI, as discussed ingreater detail with respect to FIG. 2 below. Computer system iscommunicatively coupled to a database 145 that is used for storing thethermograph data on a long term basis. As many thermographs are taken atvarious locations on the structure and over extended periods of time(e.g., minutes up to several hours), the thermograph datasets can demandsignificant memory resources of the computer system 140 (e.g., in thegigabyte (GB) to terabyte (TB) range). Periodic archival from thecomputer system 140 to the database 145 can be performed to free upmemory in the computer system 140.

In some implementations, it is preferable for inspection personnel toobserve certain rules of thumb to achieve the best possible results. Forexample, it is preferable to perform IR inspections after sunset toprevent solar reflections from insulation covering. It is alsopreferable to account for, and minimize, thermal emissions andreflections from external sources, such as the ground.

FIG. 2 is a schematic illustration of a method of thermal imageprocessing with a neural network for identification of CUI according toan embodiment of the present invention. In terms of a high-leveloverview, the method includes three sequential stages: a data capturestage 202, followed by an image processing and analysis stage 204, whichis followed by a validation stage 206. Information obtained by thevalidation stage 206 is fed back to the image processing and analysisstage 204. The data capture stage 202 encompasses setting up anapparatus to capture thermal image data from a structure of interest aswell as determining environmental condition data. For example, the datacapture stage 202 can involve setting up the infrared camera 212 toacquire images of a particular resolution for a selected duration,installing the IR camera 214 in the vicinity of the structure ofinterest or on a moving vehicle (e.g., a drone), defining a particulartarget area 216 on the structure for capturing of thermal images, anddetermining environmental parameters 218 such as humidity, air pressure,wind speed, etc.

The image processing and analysis phase 204 includes inputting thethermographs image data into a machine learning algorithm thatclassifies the images of the target area of the structure into variouscategories and enables an initial identification of whether the imagesshow an anomalous condition 222 at the target area. An anomalouscondition is an indication that CUI is possibly present in the targetarea of the structure. During a training process, the initialdetermination is not conclusive because the thermographs are being usedas training data. In the subsequent validation process 206, a number oftechniques can be used to establish whether the initial determination ofthe machine learning algorithm is correct. The various techniques caninclude pulsed eddy current evaluation 232, visual inspection 234,insulation removal 236, ultrasonic testing of wall thinning 238, anycombination of these techniques and others without limitation, to form adefinitive conclusion as to whether CUI is present in the target area ofthe structure. Thus, during the learning process, either destructive ornon-destructive techniques can be used to validate whether CUI ispresent.

Discrepancies between the initial identification and the validation arefed back into the machine learning algorithm 224 to update theactivations (the weights used for the various input parameters of thethermographs and environmental data) with the aim of optimizing thealgorithm according to the techniques of a supervised learning process.Once the accuracy of the machine learning algorithm has improved to asufficient level, validation is no longer performed and the imageprocessing and analysis stage 204 is used to detect and identify CUIdirectly. The image processing and analysis stage can employ any one ormore of a variety of supervised machine learning algorithms andtechniques including, without limitation, convolutional neural networks,recurrent neural networks, ensemble learning and boosting methods suchas Adaboost, decision trees, and support vector machines.

In one advantageous embodiment, a convolutional neural network (CNN) isused to hierarchically classify the captured thermograph data. This isfollowed by processing thermograph data captured over a significantduration of time using a recurrent neural network (CNN). In someimplementations, a boosting algorithm can be used in conjunction withthe CNN and RNN in order to achieve higher accuracies. While theboosting algorithm increases the overall number of computations, andthus increases computational time, additional accuracy is a moresignificant factor due to the high cost of misidentification.

In some embodiments of the present invention, a preprocessing phase isperformed. In the preprocessing phase, inputs to the neural networkalgorithms, including thermograph and environmental data, are encodedand/or normalized. Variables that are categorical (i.e., that arelimited to a small number of possible discrete values) can beone-hot-encoded before being fed to the neural network. Alternatively,variables that are continuous (i.e., can have a large number of possiblevalues) are normalized, and the means of the continuous variables areshifted to zero before being fed into the neural network.

The output of the preprocessing phase is an amalgamation of all of theinputs. The amalgamation can be achieved using various techniques (orcombinations thereof) including, for example: a) concatenating variablesto each other (e.g., appending environmental variables tothermographs—as if they are an extended part of the images); b)flattening each of the inputs into an n-dimensional vector and thenconcatenating the n-dimensional vectors into a single long vector;and/or c) applying an encoder, which can be implemented using anadditional network, to compress or reduce all of the inputs into asingle input before feeding it into the main network. All inputvariables, including thermographs data, environmental, or othervariables, undergo the same preprocessing procedure. The CNN operates toemphasize and highlight relevant variables relative to less relevantvariables.

A schematic illustration of an exemplary convolutional neural network(CNN) 300 that can be used in the context of the present invention isshown in FIG. 3. In the example shown, CNN 300 receives as input alocalized section of an image 302. As shown, CNN 300 includes threehierarchical levels 312, 314, 316. It is noted that fewer or a largernumber of hierarchical levels can be used. The first hierarchical level312 includes three parallel processing paths, each processing path inturn including three distinct processing stages. This complex scheme canbe clarified by explanation of the stages of a single processing path ata single level. Referring now to the leftmost path at the firsthierarchical level, a first convolutional stage 322 applies a firstconvolution function (filter) to the input image data. It is noted thatthe other processing paths operate on another localized section of theinput image. Each hierarchical level can apply a different convolutionfunction to the data it receives to better identify features in theimage. The filters can, for example, blur contrasts between neighboringimage values by averaging, or, conversely, some filters can enhancedifferences to clarify edges. Each filter composes a local patch oflower-level features into higher-level representation. In this manner,edges can be discerned from pixels, shapes from can be discerned fromedges, and so on. An example of how convolution can be applied to imagedata is shown in FIG. 4. In FIG. 4, a 5×5 square sample of pixel values410 is shown to which a convolution matrix 420, or ‘window’ can beapplied by sliding the convolution matrix 420 over the values of thesample values 410. In the example shown, the convolution matrix is a 3×3matrix function that multiplies all values along the diagonals by oneand values not along the diagonals by zero. The sum of each 3×3 sectionof the image sample as acted upon by the convolution matrix is providedto an output matrix 430. The output matrix 430 is then fed as output tothe next stage of the hierarchical layer.

The next stage of hierarchical layer 312 applies a non-linear function324 to the data of the convolutional stage, such as a ReLU (rectifiedlinear unit) or tan h function. This stage can be represented asy_(i,j)=ƒ(a_(i,j)), in which ƒ represents the non-linear function anda_(i,j) represents is a pixel of the ith row and jth column from theoutput matrix of the convolution stage. The output of the non-linearfunction stage 324 is thus a modified version of the matrix output fromconvolutional stage 322. The final stage of hierarchical level 312 is apooling stage 326 that can be used to simplify the data. For example,the pooling stage can apply a maximum function to output only themaximum value of the non-linear function of a number of rows and columnsof pixels of the output matrix from the non-linear stage. Aftersimplifying the data, the outputs of the pooling stages of all of thethree processing paths can be summed and then input to the convolutionstage 332 of one of the processing paths of the next hierarchical layer314. In hierarchical layer 314, similar or different convolutionmatrices can be used to process the data received from the firsthierarchical layer 312, and the same or different non-linear functionsand simplification functions can be used in the following non-linearstage 334 and pooling stage 336. Output from the parallel processingpaths of the second hierarchical layer 314 can be similarly pooled andthen provided as an output matrix to the third hierarchical layer 316,in which further processing takes place. The final output 350 can beinterpreted as a class label probability, or put another way, the mostlikely classification for the image. Classifications can includedifferent types of hot spots indicative of temperature differentials andpossible CUI.

The CNN learns by validation and backward propagation. This isequivalent to setting values of the output 350 and then running thealgorithm backwards from the higher hierarchical layers to the lowerlayers and modifying the convolution matrices to yield better resultsusing an optimization function. After training, the CNN should be ableto accurately classify an input thermograph into one of the presetcategories such as a hot spot, non-hot spot, etc.

While the CNN is an efficient and useful methodology for stratifyinginput images into abstraction levels according to the thermograph imagetopology, it is not best suited for detecting patterns over time.Embodiments of the present invention therefore employ a recurrent neuralnetwork (RNN) in association with the CNN to improve time-based patternrecognition.

FIG. 5 is a schematic illustration of an exemplary recurrent neuralnetwork (RNN) 500 that can be used in the context of the presentinvention. The RNN 500 includes a number of layers of which three layers502, 504, 506 are explicitly shown. The RNN is best explained withreference to the second layer 504. In this layer, x_(t) is the input tothe layer at time step t. The input x_(t) 512 can be a vector or matrixof values. S_(t) 514 represents the hidden state at time step t. Thehidden state can be considered as the “memory” of the RNN. The hiddenstate is calculated based on the previous hidden state and the input atthe current step: s_(t)=f(Ux_(t)+Ws_(t−1)). The function f is atypically a nonlinear function such as tan h or ReLU. The first hiddenstate is typically initialized to all zeroes. S_(t) is modified byparameter vector V to yield O_(t), which is the output at step t. O_(t)can be interpreted as a matrix or vector of probabilities for the nextstate s+1. The RNN 500 shares the same parameters (U, V, W above) acrossall steps. This reflects the fact that the same task at each step isperformed at each step but with different inputs. This reduces the totalnumber of parameters to learn, and thus also reduces processing time.While in the example shown, each layer has outputs at each time step,this is not necessary as in some implementation only the final output isof interest.

The RNN can be used to detect changes to thermographs over time, and toaccount for environmental variables. These variables can be introducedas parameters into the RNN along with thermograph data. The mostimportant variables to consider can be split into four main categories:i) ambient conditions, ii) conditions of the structure of interest, iii)conditions of any CUI identified and iv) configuration of the IR camerawith respect to the structure. For example, ambient conditions toaccount for in the analysis include, without limitation, the ambienttemperature over time, fluid temperature within the structure, weatherconditions including precipitation, dust, and wind speed, the time ofyear, and the amount of sunlight exposure in the location. Theconditions of the structure include, without limitation, the dimensionsof the structure and insulation, the insulation type and physicalproperties, arrangements of joints, elbows, dead-legs, etc., and opticalcharacteristics of the exposed surface, reflectivity of the structuralmetal, and any observed defects (e.g., voids in the insulation). Theconditions of any CUI identified include, without limitation, thelocation (up or down) and growth direction of the CUI, and how the CUIis distributed with respect to unimpaired areas. The main factor of theconfiguration of the IR camera is the distance between the IR camera andthe structure.

Using the information of the tendency of the various thermographs andconditions over time, further levels of analysis can be conducted. Forexample, the analysis can focus on: how the temperature difference data(e.g., hot spots, anomalies) at various locations on the structure arerelated or distinguishable; the overall tendency of the temperature andanomalies over time; whether the features that change over time appear,disappear or degrade; whether effects are more probably due toextraneous emissivity and reflections rather than CUI conditions.

In some embodiments, a boosting algorithm, such as Adaboost, can be usedin conjunction with CNNs and RNNs in order to achieve higher accuraciesat the expense of additional computation. Boosting is typically used forcombining and improving “weak learners”, which are machine learningalgorithms that, even after training, have a high error rateidentification, into a “strong” learner. Adaboost combines the output ofthe weak learning algorithms into a weighted sum that represents thefinal output of the boosted classifier. The weight of any givenalgorithm is based on the accuracy of that algorithm. While CNNs andRNNs can generally be trained to be strong learners, it can beadvantageous to add boosting to further ensure accuracy because mistakescan be extremely costly. Increasing accuracy at the sacrifice ofcomputational time can be an acceptable trade-off. In addition, boostingcan be useful in the designing phase for testing CNNs and RNNs.

The present invention provides a number of advantages that enableaccurate detection of CUI. Water and moisture spots can normally dryeasily without being detected on time. However, by continuouslymonitoring via IR imaging, it is possible to capture and identifyvulnerable locations that confine water and moisture spots. The methodsof the present invention enable budgeting of annual inspection plan thatcan be implemented according to the monitoring outcomes and imageprocessing results. The monitoring methods also are intrinsically safeand the automation of the method eliminates manual inspection errors. Aslong as the temperature in the pipe is sufficiently different than theambient temperature, the IR evaluation method generally performs up torequired standards. Anomalies found by IR can be evaluated further usingother techniques (e.g., pulsed eddy current (PEC)) using a correlativeapproach. Additionally, IR imaging can be used in cold-weatherapplications to detect insulation failures and icing.

It is to be understood that any structural and functional detailsdisclosed herein are not to be interpreted as limiting the systems andmethods, but rather are provided as a representative embodiment and/orarrangement for teaching one skilled in the art one or more ways toimplement the methods.

The methods described herein may be performed by software in machinereadable form on a tangible storage medium e.g. in the form of acomputer program comprising computer program code means adapted toperform all the steps of any of the methods described herein when theprogram is run on a computer and where the computer program may beembodied on a computer readable medium. Examples of tangible storagemedia include computer storage devices comprising computer-readablemedia such as disks, thumb drives, memory etc. and do not includepropagated signals. Propagated signals may be present in a tangiblestorage media, but propagated signals per se are not examples oftangible storage media. The software can be suitable for execution on aparallel processor or a serial processor such that the method steps maybe carried out in any suitable order, or simultaneously.

It is to be further understood that like numerals in the drawingsrepresent like elements through the several figures, and that not allcomponents and/or steps described and illustrated with reference to thefigures are required for all embodiments or arrangements

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising”, when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

Terms of orientation are used herein merely for purposes of conventionand referencing, and are not to be construed as limiting. However, it isrecognized these terms could be used with reference to a viewer.Accordingly, no limitations are implied or to be inferred.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications will be appreciated by those skilled in theart to adapt a particular instrument, situation or material to theteachings of the invention without departing from the essential scopethereof. Therefore, it is intended that the invention not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this invention, but that the invention will include allembodiments falling within the scope of the appended claims.

What is claimed is:
 1. A computer-implemented method for identifyingcorrosion under insulation (CUI) in a structure using filters trained bya first machine learning system to a first threshold for CUIclassification accuracy, comprising: receiving a thermograph capturedfrom the structure using an infrared radiation sensor and additionaldata related to the structure and environmental conditions; inputtingoutputs of the first machine learning system and additional data relatedto the structure and environment conditions into a second machinelearning system that includes a boosting algorithm, the second machinelearning system being arranged to incorporate information from earlierstates into current states; training the second machine learning systemto identify CUI according to changes in the outputs of the first machinelearning system and the additional data over time until a secondthreshold for CUI classification accuracy is reached; and after thesecond threshold is reached, identifying CUI in the structure based onreceived thermograph and additional data using the first and secondmachine learning systems in coordination.
 2. The computer-implementedmethod of claim 1, wherein the first machine learning system includes aconvolutional neural network.
 3. The computer-implemented method ofclaim 1, wherein the boosting algorithm comprises an AdaBoost adaptiveboosting algorithm arranged to increase accuracy of the second machinelearning system.
 4. The computer-implemented method of claim 1, whereinthe second machine learning system includes a recurrent neural network.5. The computer-implemented method of claim 4, wherein the additionaldata includes ambient temperature, physical characteristics of thestructure and weather conditions measured over time.
 6. Thecomputer-implemented method of claim 5, wherein the first and secondmachine learning systems are trained to recognize false positivefindings relative to reflection of infrared radiation from objectsexternal from the structure.
 7. The computer-implemented method of claim1, wherein identification of CUI includes identifying vulnerable areasof the structure under the insulation that confine moisture with a highlikelihood.
 8. The computer-implemented method of claim 1, furthercomprising: validating an initial CUI classification by an inspection ofthe structure, wherein validation is performed using at least two ofpulsed eddy current evaluation, visual inspection, insulation removaland ultrasonic testing of wall thinning.
 9. The computer-implementedmethod of claim 1, further comprising preprocessing the thermograph dataand the additional data to encode categorical variables and normalizecontinuous variables.
 10. A system for identifying corrosion underinsulation (CUI) in a structure using filters trained by a first machinelearning system to a first threshold for CUI classification accuracy,comprising: a computer system including a processor, memory and acommunication module, the processor being configured to execute aprogram that performs steps of: receiving a thermograph captured fromthe structure using an infrared radiation sensor and additional datarelated to the structure and environmental conditions; inputting outputsof the first machine learning system and additional data related to thestructure and environment conditions into a second machine learningsystem that includes a boosting algorithm, the second machine learningsystem being arranged to incorporate information from earlier statesinto current states; training the second machine learning system toidentify CUI according to changes in the outputs of the first machinelearning system and the additional data over time until a secondthreshold for CUI classification accuracy is reached; and after thefirst and second thresholds are reached, identifying CUI in thestructure based on current thermograph and additional data using thefirst and second machine learning systems in coordination.
 11. Thesystem of claim 10, wherein the first machine learning system includes aconvolutional neural network.
 12. The system of claim 10, wherein theboosting algorithm comprises an AdaBoost adaptive boosting algorithmarranged to increase accuracy of the second machine learning system. 13.The system of claim 10, wherein the second machine learning systemincludes a recurrent neural network.
 14. The system of claim 10, whereinthe additional data includes ambient temperature, physicalcharacteristics of the structure and weather conditions measured overtime.
 15. The system of claim 10, wherein the first and second machinelearning systems are trained to recognize false positive findingsrelative to reflection of infrared radiation from objects external fromthe structure.
 16. The system of claim 10, wherein identification of CUIincludes identifying vulnerable areas of the structure under theinsulation that confine moisture with a high likelihood.
 17. The systemof claim 10, wherein the processor is configured to execute the programthat performs a further step of: validating an initial CUIclassification by an inspection of the structure, wherein validation isperformed using at least two of pulsed eddy current evaluation, visualinspection, insulation removal and ultrasonic testing of wall thinning.18. The system of claim 10, wherein the computer system preprocesses thethermograph data and the additional data to encode categorical variablesand normalize continuous variables.